Some face recognition systems often use three-dimensional sensors to acquire facial information. These 3D sensors include, but are not limited to, lidar systems (i.e., laser radar), structured light sensors, RGB-D cameras, and other three-dimensional sensors. Such three-dimensional sensors (or other remote sensing technologies) record light reflected from an illuminated target to facilitate the identification of the illuminated target (e.g., a human subject, etc.). Typical face recognition systems may generate three-dimensional (“3D”) scans (sometimes also referred to as 3D images) of the subjects based on information acquired and/or measured during a sensor scan, such as 3D scans of a target subject's face and/or aspects of the target subject's face. The typical face recognition systems may perform various comparisons between the generated 3D scan and 3D scans or other images of previously acquired or known subjects in order to identify subjects.
Three-dimensional scans of subjects are perceived to provide better face recognition (“FR”) performance than two-dimensional (“2D”) images. This is because 3D scans, unlike 2D images, include depth information of a surface of the subject's face. Additionally, some 3D sensors, such as lidar systems, are typically more resilient to illumination and distance than typical 2D sensors (e.g., cameras, video cameras, etc.).
Even so, performance of 3D facial recognition systems can be negatively impacted by a varying degree due to several factors, one of which is facial occlusion. Depending on the environment under which a 3D scan is acquired, some commonly observed occlusions include, but are not limited to: hands, eyeglasses, hats, scarves, cellphones, hair, etc.
Detecting such occlusions in 2D images may be less challenging than doing so in 3D scans. This is because various texture processing techniques may be utilized to classify skin, facial hair, and non-skin region(s) in the 2D images. However, such texture information is typically not available in 3D scans.
What is needed is an improved system and method for detecting and removing occlusions in a three-dimensional scan.